{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "71fbfca2",
"metadata": {},
"outputs": [],
"source": [
"from transformers import AutoModelForSeq2SeqLM\n",
"from peft import PeftModel, PeftConfig\n",
"import torch\n",
"from datasets import load_dataset\n",
"import os\n",
"from transformers import AutoTokenizer\n",
"from torch.utils.data import DataLoader\n",
"from transformers import default_data_collator, get_linear_schedule_with_warmup\n",
"from tqdm import tqdm\n",
"from datasets import load_dataset\n",
"\n",
"dataset_name = \"twitter_complaints\"\n",
"text_column = \"Tweet text\"\n",
"label_column = \"text_label\"\n",
"batch_size = 8\n",
"\n",
"peft_model_id = \"smangrul/twitter_complaints_bigscience_T0_3B_LORA_SEQ_2_SEQ_LM\"\n",
"config = PeftConfig.from_pretrained(peft_model_id)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "cc55820a",
"metadata": {},
"outputs": [],
"source": [
"peft_model_id = \"smangrul/twitter_complaints_bigscience_T0_3B_LORA_SEQ_2_SEQ_LM\"\n",
"max_memory = {0: \"6GIB\", 1: \"0GIB\", 2: \"0GIB\", 3: \"0GIB\", 4: \"0GIB\", \"cpu\": \"30GB\"}\n",
"config = PeftConfig.from_pretrained(peft_model_id)\n",
"model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, device_map=\"auto\", max_memory=max_memory)\n",
"model = PeftModel.from_pretrained(model, peft_model_id, device_map=\"auto\", max_memory=max_memory)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e1a3648b",
"metadata": {},
"outputs": [],
"source": [
"from datasets import load_dataset\n",
"\n",
"dataset = load_dataset(\"ought/raft\", dataset_name)\n",
"\n",
"classes = [k.replace(\"_\", \" \") for k in dataset[\"train\"].features[\"Label\"].names]\n",
"print(classes)\n",
"dataset = dataset.map(\n",
" lambda x: {\"text_label\": [classes[label] for label in x[\"Label\"]]},\n",
" batched=True,\n",
" num_proc=1,\n",
")\n",
"print(dataset)\n",
"dataset[\"train\"][0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fe12d4d3",
"metadata": {},
"outputs": [],
"source": [
"tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
"target_max_length = max([len(tokenizer(class_label)[\"input_ids\"]) for class_label in classes])\n",
"\n",
"\n",
"def preprocess_function(examples):\n",
" inputs = examples[text_column]\n",
" targets = examples[label_column]\n",
" model_inputs = tokenizer(inputs, truncation=True)\n",
" labels = tokenizer(\n",
" targets, max_length=target_max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\"\n",
" )\n",
" labels = labels[\"input_ids\"]\n",
" labels[labels == tokenizer.pad_token_id] = -100\n",
" model_inputs[\"labels\"] = labels\n",
" return model_inputs\n",
"\n",
"\n",
"processed_datasets = dataset.map(\n",
" preprocess_function,\n",
" batched=True,\n",
" num_proc=1,\n",
" remove_columns=dataset[\"train\"].column_names,\n",
" load_from_cache_file=True,\n",
" desc=\"Running tokenizer on dataset\",\n",
")\n",
"\n",
"train_dataset = processed_datasets[\"train\"]\n",
"eval_dataset = processed_datasets[\"train\"]\n",
"test_dataset = processed_datasets[\"test\"]\n",
"\n",
"\n",
"def collate_fn(examples):\n",
" return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n",
"\n",
"\n",
"train_dataloader = DataLoader(\n",
" train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=batch_size, pin_memory=True\n",
")\n",
"eval_dataloader = DataLoader(eval_dataset, collate_fn=collate_fn, batch_size=batch_size, pin_memory=True)\n",
"test_dataloader = DataLoader(test_dataset, collate_fn=collate_fn, batch_size=batch_size, pin_memory=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b33be5e6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"@NYTsupport i have complained a dozen times & yet my papers are still thrown FAR from my door. Why is this so hard to resolve?\n",
"{'input_ids': tensor([[25335, 1499, 3, 10, 3320, 12056, 382, 20390, 3, 23,\n",
" 43, 25932, 3, 9, 9611, 648, 3, 184, 4624, 117,\n",
" 780, 82, 5778, 33, 341, 3, 12618, 377, 4280, 45,\n",
" 82, 1365, 5, 1615, 19, 48, 78, 614, 12, 7785,\n",
" 58, 16229, 3, 10, 3, 1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}\n",
"tensor([[ 0, 10394, 1]], device='cuda:0')\n",
"['complaint']\n"
]
}
],
"source": [
"model.eval()\n",
"i = 15\n",
"inputs = tokenizer(f'{text_column} : {dataset[\"test\"][i][\"Tweet text\"]} Label : ', return_tensors=\"pt\")\n",
"print(dataset[\"test\"][i][\"Tweet text\"])\n",
"print(inputs)\n",
"\n",
"with torch.no_grad():\n",
" outputs = model.generate(input_ids=inputs[\"input_ids\"].to(\"cuda\"), max_new_tokens=10)\n",
" print(outputs)\n",
" print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b6d6cd5b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/7 [00:00<?, ?it/s]You're using a T5TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:10<00:00, 1.48s/it]\n"
]
}
],
"source": [
"model.eval()\n",
"eval_preds = []\n",
"for _, batch in enumerate(tqdm(eval_dataloader)):\n",
" batch = {k: v.to(\"cuda\") for k, v in batch.items() if k != \"labels\"}\n",
" with torch.no_grad():\n",
" outputs = model.generate(**batch, max_new_tokens=10)\n",
" preds = outputs.detach().cpu().numpy()\n",
" eval_preds.extend(tokenizer.batch_decode(preds, skip_special_tokens=True))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "61264abe",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"accuracy=100.0\n",
"eval_preds[:10]=['no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint', 'no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint']\n",
"dataset['train'][label_column][:10]=['no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint', 'no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint']\n"
]
}
],
"source": [
"correct = 0\n",
"total = 0\n",
"for pred, true in zip(eval_preds, dataset[\"train\"][label_column]):\n",
" if pred.strip() == true.strip():\n",
" correct += 1\n",
" total += 1\n",
"accuracy = correct / total * 100\n",
"print(f\"{accuracy=}\")\n",
"print(f\"{eval_preds[:10]=}\")\n",
"print(f\"{dataset['train'][label_column][:10]=}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a70802a3",
"metadata": {},
"outputs": [],
"source": [
"model.eval()\n",
"test_preds = []\n",
"\n",
"for _, batch in enumerate(tqdm(test_dataloader)):\n",
" batch = {k: v for k, v in batch.items() if k != \"labels\"}\n",
" with torch.no_grad():\n",
" outputs = model.generate(**batch, max_new_tokens=10)\n",
" preds = outputs.detach().cpu().numpy()\n",
" test_preds.extend(tokenizer.batch_decode(preds, skip_special_tokens=True))\n",
" if len(test_preds) > 100:\n",
" break\n",
"test_preds"
]
}
],
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"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
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"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.5 (v3.10.5:f377153967, Jun 6 2022, 12:36:10) [Clang 13.0.0 (clang-1300.0.29.30)]"
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"vscode": {
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